Exploring and analyzing the improvement mechanism of U-Net and its application in medical image segmentation_Journal of Biomedical Engineering

Authors：

ZHOU Tao ^1,2 ,  HOU Senbao ¹ , LU Huiling ³ , ZHAO Yanan ¹ , DANG Pei ¹ , DONG Yali ¹

1. School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, P. R. China;
2. Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan 750021, P. R. China;
3. School of Science, Ningxia Medical University, Yinchuan 750004, P. R. China;

Corresponding author：

HOU Senbao, Email: hsb378093739@163.com

Keywords：

U-Net; Medical image; Semantic segmentation; Improvement mechanism; Network structure

DOI：

10.7507/1001-5515.202111010

Video：

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Abstract Full text Figures/Tables Video References Cited by

Remarkable results have been realized by the U-Net network in the task of medical image segmentation. In recent years, many scholars have been researching the network and expanding its structure, such as improvement of encoder and decoder and improvement of skip connection. Based on the optimization of U-Net structure and its medical image segmentation techniques, this paper elucidates in the following: First, the paper elaborates on the application of U-Net in the field of medical image segmentation; Then, the paper summarizes the seven improvement mechanism of U-Net: dense connection mechanism, residual connection mechanism, multi-scale mechanism, ensemble mechanism, dilated mechanism, attention mechanism, and transformer mechanism; Finally, the paper states the ideas and methods on the U-Net structure improvement in a bid to provide a reference for later researches, which plays a significant part in advancing U-Net.

Citation： ZHOU Tao, HOU Senbao, LU Huiling, ZHAO Yanan, DANG Pei, DONG Yali. Exploring and analyzing the improvement mechanism of U-Net and its application in medical image segmentation. Journal of Biomedical Engineering, 2022, 39(4): 806-825. doi: 10.7507/1001-5515.202111010 Copy

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1. Shichung L, Shyhliang L, Jyhshysn L, et al. Artificial convolution neural network techniques and applications for lung nodule detection. IEEE Trans Med Imaging, 1995, 14(4): 711-718.
2. LeCun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition. Proc IEEE, 1998, 86(11): 2278-2324.
3. Krizhevsky A, Sutskever I, Hinton G. Imagenet classification with deep convolutional neural networks. Commun Acm, 2017, 60(6): 84-90.
4. Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation// Medical Image Computing and Computer-assisted Intervention. Munich: Springer Lncs, 2015: 234-241.
5. Liu Liangliang, Cheng Jianhong, Quan Quan, et al. A survey on u-shaped networks in medical image segmentations. Neurocomputing, 2020, 409: 244-258.
6. Milletari F, Navab N, Ahmadi S, et al. V-Net: fully convolutional ceural networks for volumetric medical image segmentation// 2016 Fourth International Conference on 3D Vision. Stanford: 3DV, 2016: 565-571.
7. Zhou Z, Md S, Nima T, et al. Unet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans Med Imaging, 2020, 39(6): 1856-1867.
8. Oktay O, Schlemper J, Loic L F, et al. Attention U-Net: learning where to Look for the Pancreas (2018-5-30) [2021-11-04]. https://arxiv.org/pdf/1804.03999.pdf.
9. Ma Hao, Zou Yanni, Liu P. MHSU-net: a more versatile neural network for medical image segmentation. Comput Meth Prog Bio, 2021, 208(2): 106230.
10. Peng Dunlu, Xiong Shiyong, Peng Wenjia, et al. LCP-net: a local context-perception deep neural network for medical image segmentation. Expert Syst Appl, 2021, 168: 114234.
11. Chen Cheng, Liu Bo, Zhou Kangneng, et al. CSR-net: cross-scale residual network for multi-objective scaphoid fracture segmentation, Comput Biol Med, 2021, 137: 104776.
12. 周涛, 董雅丽, 霍兵强, 等. U-Net网络医学图像分割应用综述. 中国图象图形学报, 2021, 26(9): 2058-2077.
13. Cicek O, Abdulkadir A, Abdulkadir A, et al. 3D U-Net: learning dense volumetric segmentation from sparse annotation// Medical Image Computing and Computer-assisted Intervention. Athens: Springer Cham, 2016, 9901: 424-432.
14. Kumar A, Neha U, Ghosal P, et al. CSNet: A new DeepNet framework for ischemic stroke lesion segmentation. Comput Meth Prog Bio, 2020, 193: 105524.
15. Wang Y, Zhao Z, Syh B. CLCU-Net: cross-level connected U-shaped network with selective feature aggregation attention module for brain tumor segmentation. Comput Meth Prog Bio, 2021, 207: 106154.
16. Zhou Tao, Dong Yali, Lu Huiling, et al. APU-Net: an attention mechanism parallel U-Net for lung tumor segmentation. Biomed Res Int, 2022, 2022: 5303651.
17. Zhao Junting, Dang Meng, Chen Zhihao, et al. DSU-Net: distraction-sensitive U-Net for 3D lung tumor segmentation. Eng Appl Artif Intel, 2022, 109: 104649.
18. Xie Xiwang, Zhang Weidong, Wang Huadeng, et al. Dynamic adaptive residual network for liver CT image segmentation. Comput Electr Eng, 2021, 91: 107024.
19. Jiang Huiyan, Shi Tianyu, Bai Zhiqi, et al. AHCNet: an application of attention mechanism and hybrid connection for liver tumor segmentation in ct volumes. IEEE Access, 2019, 7: 24898-24909.
20. Belh K, Naima K, Nabil M, et al. Breast cancer: one-stage automated detection, segmentation, and classification of digital mammograms using unet model based-semantic segmentation. Biomed Signal Proces, 2021, 66: 102481.
21. Zhang Xiaoxuan, Zhu Xiongfeng, Tang Kai, et al. DDTNet: a dense dual-task network for tumor-infiltrating lymphocyte detection and segmentation in histopathological images of breast cancer. Med Image Anal, 2022, 78: 102415.
22. Zhou Tao, Ye Xinyu, Lu Huilin, et al. Dense Convolutional Network and its application in medical image analysis. Biomed Res Int, 2022, 2022: 2384830.
23. Xu Jiangtao, Lu Kaige, Shi xingping, et al. DenseUnet generative adversarial network for near-infrared face image colorization. Signal Process, 2021, 183: 108007.
24. Huang Gao, Liu Zhuang, Maaten L V, et al. Densely Connected Convolutional Networks// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE, 2017: 2261-2269.
25. Li Chen, Tan Yusong, Chen Wei, et al. ANU-Net: Attention-based nested U-Net to exploit full resolution features for medical image segmentation. Comput Graph, 2020, 90: 11-20.
26. Tang Pin, Zu Chen, Hong Mei, et al. DA-DSUnet: dual attention-based dense SU-NET for automatic head-and-neck tumor segmentation in MRI images. Neurocomputing, 2021, 435: 103-113.
27. Manal G, Mohamed A, Fernando C. DU-Net: Convolutional network for the detection of arterial calcifications in mammograms. IEEE Trans Med Imaging, 2020, 39(10): 3240-3249.
28. Luo Zhongming, Zhang Yu, Zhou Lei. Micro-vessel image segmentation based on the AD-UNet model. IEEE Access, 2019, 7: 143402-143411.
29. Ke Liangru, Deng Yishu, Xia Weixiong, et al. Development of a self-constrained 3D denseNet model in automatic detection and segmentation of nasopharyngeal carcinoma using magnetic resonance images. Oral Oncol, 2020, 110: 104862.
30. Nasser A, Amr A, AbdAllah E, et al. Efficient 3D deep learning model for medical image semantic segmentation. Alex Eng J, 2021, 60(1): 1231-1239.
31. Zhang Ziang, Wu Chendong, Sonya C, et al. DENSE-INception U-net for medical image segmentation. Comput Meth Prog Bio, 2020, 192: 105395.
32. Zhang Jinhua, Li Chen, Kosov S. LCU-Net: A novel low-cost U-Net for environmental microorganism image segmentation. Pattern Recogn, 2021, 115(4): 107885.
33. Jose D, Christian D, Ismail B. IVD-Net: Intervertebral disc localization and segmentation in MRI with a multi-modal UNet. Lect Note Comput Sci, 2019, 11397: 130-143.
34. Zhang Jiawei, Jin Yuzhen, Xu Jilan, et al. MDU-Net: Multi-scale Densely Connected U-Net for biomedical image segmentation (2018-12-4) [2021-11-04]. https: //arxiv.org/pdf/1812.00352.pdf.
35. Wang E, Chen C, Ahmad A, et al. A deep learning based medical image segmentation technique in Internet-of-Medical-Things domain. Future Gener Comp Sy, 2020, 108: 135-144.
36. Shi Jiali, Zhang Rong, Guo Lijun, et al. Dual dense context-aware network for hippocampal segmentation. Biomed Signal Proces, 2020, 61(6): 102038.
37. Mohammad Y, Philippe J, Farida C. Dense-Unet: a light model for lung fields segmentation in chest X-ray images// 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Montreal: IEEE, 2020: 1242-1245.
38. He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al. Deep residual learning for image recognition// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2016: 770-778.
39. Liu Jin, Kang Yanqin, Qiang Jun, et al. Low-dose CT imaging via cascaded ResUnet with spectrum loss. Methods, 2021, 202: 78-87.
40. 周涛, 刘赟璨, 陆惠玲, 等. ResNet及其在医学图像处理领域的应用: 研究进展与挑战. 电子与信息学报, 2022, 44(1): 149-167.
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Exploring and analyzing the improvement mechanism of U-Net and its application in medical image segmentation

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